1. Field of the Invention
This invention relates to electronic neural network elements and networks configured therefrom.
2. Description of the Prior Art
Conventional neural network elements utilize digital electronic circuit technology to develop the multi-synaptic connection circuit configurations needed for the manufacture of high density neural networks. As the use of neural networks in the development of sophisticated computer systems progresses, the need for higher densities is a driving force.
Neural network elements are used as synapses in the building of networks which use massively parallel computing in emulation of the manner in which the human brain operates. What is needed are neural network elements and networks configured therefrom which provided the increasingly required higher densities and more accurately emulate the manner in which the human brain operates.
The various conventional net configurations have particular requirements which place limitations on the ability to configure neural network elements to form such nets. The Hopfield and Hamming nets, for example, require additional gain for the hard-limit threshold sigmoid functions used therein.
There are many examples of monolithic neural net devices which have been proposed in the literature. Many of these may be categorized as fixed connection devices with analog weightings, devices with digitally programmable connections and weights, and devices with analog programmable connections and weights. In this context it is important to note that the term digital does not refer to binary values, but rather to the techniques for setting these values.
One of the substantial obstacles to the development of neural net devices, particularly for the more general case of the development of a neural net device with analog programmable connections and weights, has been the difficulty in implementing sufficiently small individual synapses or connections. Conventional devices include synapses with internal digital to analog and analog to digital converters or internal digital memories and multipliers.
What are needed are flexibly configurable neural network elements which can be conveniently densely packaged while providing wide ranges of programming options, particularly analog programmable connections and weights.